Featured Resource

Expertise Beyond the Numbers

Predictive Planning in Oracle’s PBCS/EPBCS – Unleash the Power!

January 12, 2018SC&H Group

Predictive Planning – it sounds so simple. You predict how you’re going to do in the future, and you plan based on that prediction. But as any finance professional will tell you, it’s never that simple. Unless you are throwing darts at your financial targets, good predictions can involve rigorous historical analysis, intense statistical modeling, and broad industry analysis. Developing an initial prediction can take weeks, or even months.

But what if it didn’t have to? What if predictive planning could be available to any user, all in an application they already use?

The Predictive Planning module in Oracle’s EPM Cloud applications (PBCS and EPBCS) looks to answer this exact question. This recently added functionality allows users to optimize their planning and forecasting estimates via a rigorous, pre-built statistical modeling engine. With a few easy clicks, Predictive Planning will collect historical data, match it to one of several industry-standard statistical models, and generate a dashboard offering predictions for future results. Within this dashboard, users can compare their submitted data with the predicted data, review the data sets for accuracy, and, when appropriate, apply the predicted values directly into their plan and/or forecast.

Predictive Planning is automatically included in all PBCS/EPBCS applications and can be made available to users with minimal administrative intervention. Read on and we will walk you through the configurations to unleash Predictive Planning as a robust planning and forecasting aid and put down the darts!

Base Functionality

Predictive Planning is automatically enabled on any webform with all time dimensions in one axis, and all non-time dimensions in the other axis. To access this feature, users simply open the form, open the Actions menu, and select Predictive Planning.

Once the user makes this selection, the Predictive Planning dashboard will automatically open and complete its statistical analysis.

The dashboard includes a chart including an admin-selected historical data set (green), the predicted data (blue), the data on the form (red), and a range between the predicted best-case and worst-case growth scenarios. The dashboard also includes key metrics and raw statistical analysis on the right-hand side.

If users feel that the predicted results are more accurate than the data that is currently entered in the form, they can utilize a simple paste functionality to replace the data with the prediction, or the predicted best-case or worst-case results.

Once this data is pasted in the form, users simply need to hit the Save button, and the prediction will be added to the application.

The Predictive Planning functionality is also available as a Smart View add-in. This tool will allow the same functionality, but will also generate a printout of the statistical accuracy of each individual line item.

Comparative Analysis Use Case

In a more robust model, Predictive Planning can be utilized to ensure that user-submitted data falls within accepted thresholds. To allow for this, additional Versions can be added to the application to store the modeled prediction, best case, and worst case data.

Once this data is pasted into the form, it can be used in calculations, formulas, and dashboards. It can also be referenced in webform validations to ensure data accuracy.

In this model, the submitted data in the form is compared with the predicted best case and worst case outcomes. Cells in red contain data which fails to meet the predicted worst case threshold. Cells in green contain data which exceeds the predicted best case limit. These immediately display to the user that their data is in need of correction. They can either re-run their calculations, or simply paste the Predictive Planning result directly into the form.

These webform validations can also be used to prevent erroneous data from being included in final budgets and forecasts. With the included Approvals functionality, invalid data can trigger email notifications, redirect the approval path, or even stop data from being approved at all.

Administering Predictive Planning

To establish global settings for Predictive Planning, in the navigator menu, navigate to the System Settings and Defaults window.

In the Current Application Defaults tab, locate the Predictive Planning Options section. In this section, you can set the Scenario/Version combination that will be used for historical data purposes, and the confidence interval that will be used for the best case and worst case prediction range.

There are a few specific restrictions around the design and build of webforms for use in Predictive Planning:

The webform must have a time dimension (Period or Year) in either the row or column axis.

All non-time dimensions (excluding Scenario and Version) may not be included in the same axis as the time dimensions.

Predictive Planning functionality is available on composite forms, but the pasting functionality is not.

Predictive Planning cannot be used in Ad Hoc grids.

To use Predictive Planning in Smart View, users will need to install the Predictive Planning Smart View add-in. They can do so by clicking their name in the top right corner of the interface, and selecting Downloads.

They can then select the Predictive Planning add-in. Once they install it, the functionality will automatically appear whenever a valid webform is opened.

To open Predictive Planning within a webform in Smart View, navigate to the Planning tab in Excel, and locate the Predict button.

This will open the Predict tab. Within the Predict tab, clicking the Predict button will kick off the Predictive Planning process.

Wrapping Things Up

Predictive Planning is an excellent new tool that offers users the ability to quickly generate a rigorously statistically modeled future plan or forecast. When combined with other Oracle EPM Cloud (PBCS or EPBCS) features, this tool allows users to quickly see whether their plans are reasonable based on the historical growth trends of the business.

While not all companies may have enough historical data for the statistical modeling to provide definitive results (3-5 years of historical data is recommended), the companies that do will be well-served by adding this technology to their existing portfolio of modeling tools.